Performance Analysis of Speech Enhancement using LMS, NLMS and UNANR Algorithms

The speech enhancement is one of the effective techniques to solve speech degraded by noise, that the speech recognitions performance in noisy environment should be investigated. In this paper the approach is to estimate the speech enhancement performance with different noise reduction algorithms using adaptive filters like LMS, NLMS and UNANR. In my approach the different noise cancellation algorithms are analyses and their performance of these algorithms is estimated. The effectiveness of these noise reduction algorithms evaluated by experiments using the MATLAB R2013a software tool and develops stimulink for different noise reduction algorithms and analyses their performance to know the better noise cancellation algorithm suits in adverse noisy condition. The proposed parametric formulation describes the original method and several of its modifications. Based on the mathematical formulation, the speech spectral amplitude estimator is derived and optimized by minimizing the mean-square error (MSE) of the speech spectrum and also draw the analysis results between SNR versus PSNR with reduction of simulation time.